Predictability of individual factors within an enterprise budgeting and planning system - Presentation of survey results
Bibliographic record
Abstract
a This paper presents the initial results of a questionnaire focused on the ability of enterprises to forecast the behavior of the external environment and its influence on their budgeting processes. Traditional budgeting and planning systems have often been criticized for their inability to accurately predict the behavior of revenue streams, costs, profits, sales volumes and other indicators often affected by the behavior of the external environment. These budgets, based on annual accounting periods, frequently clash with a rapidly moving business environment and frequent changes in the market. The objective of the study presented herein was to obtain empirical evidence on the capability of Czech enterprises to predict the behavior of primary budgetary elements, such as profits or sales volumes. The results of the study are based on a survey of 145 medium-and large-sized enterprises in the Czech Republic. The findings are compared with those of a similar study performed in the USA and Canada in 2009.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".